Automated vehicle detection has been an important component of roadway and intersection operation systems for decades. Inductance loop detectors, which detect vehicles based on changes to the inductance within an inductance loop installed under a roadway caused by the vehicles as they pass over the loop, have been used since the early 1960s. Inductance loop detectors are relatively cheap in unit cost when compared with other detector types and can produce reliable traffic counts under many flow scenarios. Maintenance and installation of loop detectors, however, require lane closures that may generate significant indirect costs. Furthermore, inductance loop detectors are point detectors and only detect the presence of a vehicle in a relatively small space. Accordingly, multiple loop detectors are required to obtain traffic parameters other than the presence of a vehicle, such as speed and queue lengths. Moreover, embedding inductance loops in pavement causes damage to the pavement structure, thereby shortening pavement lifetime and increasing indirect costs.
In addition to inductance loop detectors, computer-vision approaches have been implemented to detect vehicles. Video cameras often have lower maintenance costs when compared to inductance loop detectors and are capable of providing richer traffic information than their inductance loop counterparts. However, since video-based vehicle detection algorithms are based on visual data, environmental factors and occlusions play significant roles in vehicle detection accuracy. Good visibility of objects of interest is a key assumption in video-based detection mechanisms. Environmental impacts, such as shadows, sun glare, lighting changes, and sight-disturbing conditions (e.g., heavy rain or snow), may degrade the visibility or alter the appearance of the objects in a scene. Additionally, vibration caused by, for example, wind or passing vehicles, is a common problem for mounted video cameras. The resulting movements of camera vibration often cause displacements of static objects between a current frame and a background frame and trigger a significant amount of false alarms in vehicle detection systems that rely on the ability to identify a static background within the video data. Additionally, vehicle occlusions resulting when one vehicle appears next to another and partially or completely obscures that vehicle are prevalent in most observation angles and can be difficult to overcome in computer-vision based approaches to vehicle detection.